Semantic Role Labeling

This tutorial will describe semantic role labeling, the assignment of semantic roles to eventuality participants in an attempt to approximate a semantic representation of an utterance. The linguistic background and motivation for the definition of semantic roles will be presented, as well as the basic approach to semantic role annotation of large amounts of corpora. Recent extensions to this approach that encompass light verb constructions and predicative adjectives will be included, with reference to their impact on English, Arabic, Hindi and Chinese. Current proposed extensions such as Abstract Meaning Representations and richer event representations will also be touched on. Details of machine learning approaches will be provided, beginning with fully supervised approaches that use the annotated corpora as training material. The importance of syntactic parse information and the contributions of different feature choices, including tree kernels, will be discussed, as well as the advantages and disadvantages of particular machine learning algorithms and approaches such as joint inference. Appropriate considerations for evaluation will be presented as well as successful uses of semantic role labeling in NLP applications. We will also cover techniques for exploiting unlabeled corpora and transferring models across languages. These include methods, which project annotations across languages using parallel data, induce representations solely from unlabeled corpora (unsupervised methods) or exploit a combination of a small amount of human annotation and a large unlabeled corpus (semi-supervised techniques). We will discuss methods based on different machine learning paradigms, including generative Bayesian models, graph-based algorithms and bootstrapping style techniques.